Modular type-2 neuro-fuzzy systems

Authors:
Janusz Starczewski;Rafał Scherer;Marcin Korytkowski;Robert Nowicki
Affiliations:
Department of Computer Engineering, Częstochowa University of Technology, Częstochowa, Poland and Department of Artificial Intelligence, Academy of Humanities and Economics in Lodz, ...;Department of Computer Engineering, Częstochowa University of Technology, Częstochowa, Poland and Department of Artificial Intelligence, Academy of Humanities and Economics in Lodz, ...;Department of Computer Engineering, Częstochowa University of Technology, Częstochowa, Poland and Olsztyn Academy of Computer Science and Management, Olsztyn, Poland;Department of Computer Engineering, Częstochowa University of Technology, Częstochowa, Poland and Department of Artificial Intelligence, Academy of Humanities and Economics in Lodz, ...
Venue:
PPAM'07 Proceedings of the 7th international conference on Parallel processing and applied mathematics
Year:
2007

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Abstract

In the paper we study a modular system which can be converted into a type-2 neuro-fuzzy system. The rule base of such system consists of triangular type-2 fuzzy sets. The modular structure is trained using the backpropagation method combined with the AdaBoost algorithm. By applying the type-2 neurofuzzy system, the modular structure is converted into a compressed form. This allows to overcome the training problem of type-2 neuro-fuzzy systems. An illustrative example is given to show the efficiency of our approach in the problems of classification.